displaying associations, improving alignments and gene sets at ucsc jim kent and the ucsc genome...

Displaying associations, improving alignments

and gene sets at UCSC

Displaying associations, improving alignments

and gene sets at UCSC

Jim Kent and the UCSC Genome Bioinformatics Group

Wellcome Trust Case Control Consortium rheumatoid arthritis data

Sort Genes to see candidatesSort Genes to see candidates

Case control consortium rheumatoid arthritis data,type1 diabetes and bipolar disorder. National Instituteof Mental Health bipolar disorder in US and Germanpopulations (different scale).

In the long term we hope to import data from GAINand dbGAP and other sources as well.

28-way multiple alignment28-way multiple alignment

Still based on Penn State/UCSC blastz/chain/net/multizpipeline.

Have added “syntenic” filtering for high coverage genomesand reciprocal-best filtering for 2x genomes to reduceartifacts from paralogs.

PhyloP vs. PhastConsPhyloP vs. PhastCons

Existing conservation track uses PhastCons algorithm,which computes probability that a region is conserved. Asmore species are added this converges to 0 or 1.

PhyloP track instead shows degree of conservation of a base

UCSC Genes GoalsUCSC Genes Goals• Include noncoding as well as coding

genes• Increase sensitivity of gene set in general.• Increase coverage of alternative splice

forms (but not too much).• Apply comparative genomics to protein

(CDS) prediction.• Create permanent accessions for

transcripts.

• Include noncoding as well as coding genes

• Increase sensitivity of gene set in general.• Increase coverage of alternative splice

forms (but not too much).• Apply comparative genomics to protein

(CDS) prediction.• Create permanent accessions for

transcripts.

Make graph

Snap soft ends to hard end within 6 bp

Extend soft ends to hard ends

Consensus of soft ends weighted 3/4 of way towards long

Weigh edges by number of transcripts that make them

3 233

31

12 41

Make graphs from various other sources:

4 354

53

25

63

3 233

31

12 41

exoniphyests

Mousesplicing

Merge in weights from other graphs:

Walk graph to get nonredundant transcripts, starting withfirst transcript and continuing until all edges in graph of weight above a threshold are emitted.

4 3542

56

B

C D

E

A

A

3

35

Initial transcripts (ordered by exon count)

Walk graph to get nonredundant transcripts, starting withfirst transcript and continuing until all edges in graph of weight above a threshold are emitted.

4 3542

56

B

C D

E

A

A

35

3

Walk graph to get nonredundant transcripts, starting withfirst transcript and continuing until all edges in graph of weighted above a threshold are emitted.

4 3542

56

B

C D

E

A

A

35

B

>= 3

>= 2

3

Walk graph to get nonredundant transcripts, starting withfirst transcript and continuing until all edges in graph of weighted above a threshold are emitted.

4 354 56

B

C D

E

A

A

35

B

>= 3

>= 2

3

2

DONE

Evidence type and weightsEvidence type and weights

refSeq RNA 100

Other Genbank RNA 2

Genbank spliced EST graph edges from at least 2 ESTs

1

Orthologous splicing graph in mouse mapped to human

1

Exoniphy exon predictions 1

Minimum total weight of 3 for spliced transcripts, 4 for unspliced.Minimum total weight of 3 for spliced transcripts, 4 for unspliced.

Assigning Coding RegionsAssigning Coding Regions• Take top scoring ORF using a program,

txCdsPredict, that considers:– Length of ORF– Kozak consensus sequence– Nonsense mediated decay– Upstream open reading frames– Length of orthologous ORF in other species.

• txCdsPredict agrees with RefSeq reviewed ~96% of the time.

• Take top scoring ORF using a program, txCdsPredict, that considers:– Length of ORF– Kozak consensus sequence– Nonsense mediated decay– Upstream open reading frames– Length of orthologous ORF in other species.

• txCdsPredict agrees with RefSeq reviewed ~96% of the time.

Gene StatisticsGene Statisticsclass UCSC Ensemb

lRefSeq

coding 20433 22934 18992

antisense 643 109 19

noncoding 5228 9034 590

Transcript Statistics Transcript Statistics class UCSC Ensemb

lRefSeq

coding 45475 43569 25187

nearCoding 4469 112 14

antisense 731 109 19

noncoding 6047 9045 592

Non-codingCoding

Near-coding

• 38% of UCSC noncoding genes are < 200 bp transcripts primarily of known types such as snoRNAs, piRNAs, miRNAs etc.

• 62% are long, with a size distribution much like coding.

• (For Ensemble only 21% of noncoding are long)

Long noncoding genes have lower expression levelsLong noncoding genes have lower expression levels

Absolute expression values from Affymetrix human exon arrays

Coding

Non coding

Other characteristics of long noncodingOther characteristics of long noncoding• Long noncoding have lower tissue specificity.• Poor conservation. Average phastCons score is

0.09 for long noncoding vs 0.73 for coding.• BLAST analysis suggests 20% of long

noncoding may be transcribed pseudogenes.

• Conclusion - long noncoding but transcribed genes are slippery. Most are likely nonfunctional. – Xist is poorly conserved overall but has some peaks

and is reasonably well expressed.

• Long noncoding have lower tissue specificity.• Poor conservation. Average phastCons score is

0.09 for long noncoding vs 0.73 for coding.• BLAST analysis suggests 20% of long

noncoding may be transcribed pseudogenes.

• Conclusion - long noncoding but transcribed genes are slippery. Most are likely nonfunctional. – Xist is poorly conserved overall but has some peaks

and is reasonably well expressed.

AcknowledgementsAcknowledgements• Programming and analysis:

– Galt Barber - Genome Graphs extensions– Webb Miller Lab - Alignments– Adam Seipel - Evolutionary analysis– Dorota Retelska - UCSC noncoding genes

• Data:– Sanger, Wash U, Broad, JGI, NCBI, EBI, Affy– Contributors to scientific databases

worldwide

• Funding:– NHGRI, NCI, HHMI, State of California

• Programming and analysis:– Galt Barber - Genome Graphs extensions– Webb Miller Lab - Alignments– Adam Seipel - Evolutionary analysis– Dorota Retelska - UCSC noncoding genes

• Data:– Sanger, Wash U, Broad, JGI, NCBI, EBI, Affy– Contributors to scientific databases

worldwide

• Funding:– NHGRI, NCI, HHMI, State of California

The EndThe End

UCSC Genes Overall PipelineUCSC Genes Overall Pipeline• Start with genomic/RNA alignments• Remove antibody fragments• Clean alignments and project to genome• Cluster into splicing graph• Add EST, Exoniphy, OrthoSplice info.• Walk unique well supported transcripts out of

graph.• Assign coding regions (CDS) to transcripts.• Classify into coding, antisense, noncoding.• Assign accessions.

• Start with genomic/RNA alignments• Remove antibody fragments• Clean alignments and project to genome• Cluster into splicing graph• Add EST, Exoniphy, OrthoSplice info.• Walk unique well supported transcripts out of

graph.• Assign coding regions (CDS) to transcripts.• Classify into coding, antisense, noncoding.• Assign accessions.

Classifying transcriptsClassifying transcripts• Coding: CDS survives trimming

stage• Near-coding: overlap coding by at

least 20 bases on same strand• Near-coding junk: near-coding

transcripts that show signs of incomplete splicing. These are removed.

• Antisense: overlap coding by at least 20 bases on opposite strand

• Noncoding: other transcripts

• Coding: CDS survives trimming stage

• Near-coding: overlap coding by at least 20 bases on same strand

• Near-coding junk: near-coding transcripts that show signs of incomplete splicing. These are removed.

• Antisense: overlap coding by at least 20 bases on opposite strand

• Noncoding: other transcripts

displaying associations, improving alignments and gene sets at ucsc jim kent and the ucsc genome...

Documents